# model settings
norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True)
model = dict(
    type='EncoderDecoder',
    backbone=dict(type='CGNet',
                  norm_cfg=norm_cfg,
                  in_channels=3,
                  num_channels=(32, 64, 128),
                  num_blocks=(3, 21),
                  dilations=(2, 4),
                  reductions=(8, 16)),
    decode_head=dict(type='FCNHead',
                     in_channels=256,
                     in_index=2,
                     channels=256,
                     num_convs=0,
                     concat_input=False,
                     dropout_ratio=0,
                     num_classes=19,
                     norm_cfg=norm_cfg,
                     loss_decode=dict(
                         type='CrossEntropyLoss',
                         use_sigmoid=False,
                         loss_weight=1.0,
                         class_weight=[
                             2.5959933, 6.7415504, 3.5354059, 9.8663225,
                             9.690899, 9.369352, 10.289121, 9.953208,
                             4.3097677, 9.490387, 7.674431, 9.396905,
                             10.347791, 6.3927646, 10.226669, 10.241062,
                             10.280587, 10.396974, 10.055647
                         ])),
    # model training and testing settings
    train_cfg=dict(sampler=None),
    test_cfg=dict(mode='whole'))
